Prediction uncertainty has also received some attention in medical image segmentation studies. Some studies have proposed methods to estimate the uncertainty [1]} or to use the prediction uncertainty to improve the segmentation accuracy [2]}, [3]}. However, little attention has been paid to methods for improving the calibration of CNN-based segmentation models. An example of the latter is the work of [4]}, where the authors use model ensembles to arrive at better-calibrated models. That study trained an ensemble of CCNs with random initialization of network weights and random shuffling of training data. They show that the ensemble average is better calibrated than prediction of a single method. However, their proposed method requires training and maintaining as many as 50 separate models, which is quite inefficient for many clinical applications.
